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Designed for ordered data like time series data. Optionally perform group-wise merge (see examples). Parameters ---------- left : DataFrame right : DataFrame on : label or list Field names to join on. Must be found in both DataFrames. left_on : label or list, or array-like Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns. right_on : label or list, or array-like Field names to join on in right DataFrame or vector/list of vectors per left_on docs. left_by : column name or list of column names Group left DataFrame by group columns and merge piece by piece with right DataFrame. right_by : column name or list of column names Group right DataFrame by group columns and merge piece by piece with left DataFrame. fill_method : {'ffill', None}, default None Interpolation method for data. suffixes : list-like, default is ("_x", "_y") A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in `left` and `right` respectively. Pass a value of `None` instead of a string to indicate that the column name from `left` or `right` should be left as-is, with no suffix. At least one of the values must not be None. .. versionchanged:: 0.25.0 how : {'left', 'right', 'outer', 'inner'}, default 'outer' * left: use only keys from left frame (SQL: left outer join) * right: use only keys from right frame (SQL: right outer join) * outer: use union of keys from both frames (SQL: full outer join) * inner: use intersection of keys from both frames (SQL: inner join). Returns ------- DataFrame The merged DataFrame output type will the be same as 'left', if it is a subclass of DataFrame. See Also -------- merge : Merge with a database-style join. merge_asof : Merge on nearest keys. Examples -------- >>> df1 = pd.DataFrame( ... { ... "key": ["a", "c", "e", "a", "c", "e"], ... "lvalue": [1, 2, 3, 1, 2, 3], ... "group": ["a", "a", "a", "b", "b", "b"] ... } ... ) >>> df1 key lvalue group 0 a 1 a 1 c 2 a 2 e 3 a 3 a 1 b 4 c 2 b 5 e 3 b >>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]}) >>> df2 key rvalue 0 b 1 1 c 2 2 d 3 >>> merge_ordered(df1, df2, fill_method="ffill", left_by="group") key lvalue group rvalue 0 a 1 a NaN 1 b 1 a 1.0 2 c 2 a 2.0 3 d 2 a 3.0 4 e 3 a 3.0 5 a 1 b NaN 6 b 1 b 1.0 7 c 2 b 2.0 8 d 2 b 3.0 9 e 3 b 3.0 r4 )rJ c s t | |����� �d�}|�� S )N)r@ rA rB rF rp r? )� _OrderedMergerL )�x�yrM )rp r? rA r@ rB rF rN rO �_merger s z merge_ordered.<locals>._mergerNz*Can only group either left or right framesr z not found in left columnsc s � | |�S )NrN )rr rs )rt rN rO �<lambda>6 � z merge_ordered.<locals>.<lambda>z not found in right columnsc s � || �S )NrN )rr rs )rt rN rO ru > rv ) � ValueErrorrZ r; rU � differencerQ rc r` rn ) r= r> r@ rA rB �left_by�right_byrp rF r? �checkrm �_rN )rt rp r? rA r@ rB rF rO � merge_ordered� s( h r} �backward) r= r> r@ rA rB rC rD rF �allow_exact_matches� directionrJ c C s. t | ||||||||| | d| | | d�}|�� S )a&# Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys. Both DataFrames must be sorted by the key. For each row in the left DataFrame: - A "backward" search selects the last row in the right DataFrame whose 'on' key is less than or equal to the left's key. - A "forward" search selects the first row in the right DataFrame whose 'on' key is greater than or equal to the left's key. - A "nearest" search selects the row in the right DataFrame whose 'on' key is closest in absolute distance to the left's key. The default is "backward" and is compatible in versions below 0.20.0. The direction parameter was added in version 0.20.0 and introduces "forward" and "nearest". Optionally match on equivalent keys with 'by' before searching with 'on'. Parameters ---------- left : DataFrame or named Series right : DataFrame or named Series on : label Field name to join on. Must be found in both DataFrames. The data MUST be ordered. Furthermore this must be a numeric column, such as datetimelike, integer, or float. On or left_on/right_on must be given. left_on : label Field name to join on in left DataFrame. right_on : label Field name to join on in right DataFrame. left_index : bool Use the index of the left DataFrame as the join key. right_index : bool Use the index of the right DataFrame as the join key. by : column name or list of column names Match on these columns before performing merge operation. left_by : column name Field names to match on in the left DataFrame. right_by : column name Field names to match on in the right DataFrame. suffixes : 2-length sequence (tuple, list, ...) Suffix to apply to overlapping column names in the left and right side, respectively. tolerance : int or Timedelta, optional, default None Select asof tolerance within this range; must be compatible with the merge index. allow_exact_matches : bool, default True - If True, allow matching with the same 'on' value (i.e. less-than-or-equal-to / greater-than-or-equal-to) - If False, don't match the same 'on' value (i.e., strictly less-than / strictly greater-than). direction : 'backward' (default), 'forward', or 'nearest' Whether to search for prior, subsequent, or closest matches. Returns ------- merged : DataFrame See Also -------- merge : Merge with a database-style join. merge_ordered : Merge with optional filling/interpolation. Examples -------- >>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]}) >>> left a left_val 0 1 a 1 5 b 2 10 c >>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]}) >>> right a right_val 0 1 1 1 2 2 2 3 3 3 6 6 4 7 7 >>> pd.merge_asof(left, right, on="a") a left_val right_val 0 1 a 1 1 5 b 3 2 10 c 7 >>> pd.merge_asof(left, right, on="a", allow_exact_matches=False) a left_val right_val 0 1 a NaN 1 5 b 3.0 2 10 c 7.0 >>> pd.merge_asof(left, right, on="a", direction="forward") a left_val right_val 0 1 a 1.0 1 5 b 6.0 2 10 c NaN >>> pd.merge_asof(left, right, on="a", direction="nearest") a left_val right_val 0 1 a 1 1 5 b 6 2 10 c 7 We can use indexed DataFrames as well. >>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10]) >>> left left_val 1 a 5 b 10 c >>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7]) >>> right right_val 1 1 2 2 3 3 6 6 7 7 >>> pd.merge_asof(left, right, left_index=True, right_index=True) left_val right_val 1 a 1 5 b 3 10 c 7 Here is a real-world times-series example >>> quotes = pd.DataFrame( ... { ... "time": [ ... pd.Timestamp("2016-05-25 13:30:00.023"), ... pd.Timestamp("2016-05-25 13:30:00.023"), ... pd.Timestamp("2016-05-25 13:30:00.030"), ... pd.Timestamp("2016-05-25 13:30:00.041"), ... pd.Timestamp("2016-05-25 13:30:00.048"), ... pd.Timestamp("2016-05-25 13:30:00.049"), ... pd.Timestamp("2016-05-25 13:30:00.072"), ... pd.Timestamp("2016-05-25 13:30:00.075") ... ], ... "ticker": [ ... "GOOG", ... "MSFT", ... "MSFT", ... "MSFT", ... "GOOG", ... "AAPL", ... "GOOG", ... "MSFT" ... ], ... "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01], ... "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03] ... } ... ) >>> quotes time ticker bid ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00 4 2016-05-25 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88 7 2016-05-25 13:30:00.075 MSFT 52.01 52.03 >>> trades = pd.DataFrame( ... { ... "time": [ ... pd.Timestamp("2016-05-25 13:30:00.023"), ... pd.Timestamp("2016-05-25 13:30:00.038"), ... pd.Timestamp("2016-05-25 13:30:00.048"), ... pd.Timestamp("2016-05-25 13:30:00.048"), ... pd.Timestamp("2016-05-25 13:30:00.048") ... ], ... "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"], ... "price": [51.95, 51.95, 720.77, 720.92, 98.0], ... "quantity": [75, 155, 100, 100, 100] ... } ... ) >>> trades time ticker price quantity 0 2016-05-25 13:30:00.023 MSFT 51.95 75 1 2016-05-25 13:30:00.038 MSFT 51.95 155 2 2016-05-25 13:30:00.048 GOOG 720.77 100 3 2016-05-25 13:30:00.048 GOOG 720.92 100 4 2016-05-25 13:30:00.048 AAPL 98.00 100 By default we are taking the asof of the quotes >>> pd.merge_asof(trades, quotes, on="time", by="ticker") time ticker price quantity bid ask 0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96 1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98 2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93 3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN We only asof within 2ms between the quote time and the trade time >>> pd.merge_asof( ... trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms") ... ) time ticker price quantity bid ask 0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96 1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN 2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93 3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN We only asof within 10ms between the quote time and the trade time and we exclude exact matches on time. However *prior* data will propagate forward >>> pd.merge_asof( ... trades, ... quotes, ... on="time", ... by="ticker", ... tolerance=pd.Timedelta("10ms"), ... allow_exact_matches=False ... ) time ticker price quantity bid ask 0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN 1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98 2 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN 3 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN NaN 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN �asof) r@ rA rB rC rD rf ry rz rF r? � tolerancer r� )� _AsOfMergerL )r= r> r@ rA rB rC rD rf ry rz rF r� r r� rM rN rN rO � merge_asofE s& r� c @ s e Zd ZdZdZd@d d d d d d d d d d dd d dd�dd�Zdd�dd�Zdddd�dd�Zdddd �d d �Zddd �d d!�Z ddd �d"d#�Z dd$d$dd%�d&d'�Z d(d�d)d*�Z d+d�d,d-�Z dAd/d/d0d d/d1�d2d3�Zd4d5� Zdd�d6d7�Zddd8d �d9d:�Zdd�d;d<�Zd dd=�d>d?�ZdS )BrK z� Perform a database (SQL) merge operation between two DataFrame or Series objects using either columns as keys or their row indexes rP r7 N� FT�r9 r: zDataFrame | Seriesr; zIndexLabel | None�intr< r z str | None)r= r> r? r@ rA rB �axisrC rD rE rF rG rH rI c C s� t |�}t |�}| | _| _| | _| _|| _|| _| jjdkrHd| nd| _t � |�| _ t � |�| _ t � |�| _ | | _| | _| | _|| _| | _| | _| t| jt�r�| j| _n&t| jt�r�| jr�dnd | _ntd��t|�s�tdt|�� ���t| ��s tdt| �� ���|jj |jj k�rHd|jj � d |jj � d �}t j |t d d � | � � d }| jd k�r�| �!| j| j�\| _| _| _}|g | _ | _ || _"| �#� \| _$| _%| _&| �'� |d k �r�| �(|� d S )N� r� r �_mergez<indicator option can only accept boolean or string argumentsz/left_index parameter must be of type bool, not z0right_index parameter must be of type bool, not zYmerging between different levels is deprecated and will be removed in a future version. 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Zbm_axis�ndimr� �comZmaybe_make_listr@ rA rB rG rF rE rC rD rH rZ r; �indicator_namer< rw r �typerQ �nlevels�warnings�warn� FutureWarning�_validate_specification�_create_cross_configuration�_cross�_get_merge_keys�left_join_keys�right_join_keys� join_names�_maybe_coerce_merge_keys� _validate)�selfr= r> r? r@ rA rB r� rC rD rE rF rG rH rI �_left�_right�msg� cross_colrN rN rO �__init__b sT z_MergeOperation.__init__r4 )rJ c C s� | j r | �| j| j�\| _| _| �� \}}}t| jj| jj| j�\}}|d k rVd|ini }|d k rjd|ini }t| jj |f| jj |fg|� |�|gd| j d�}| jj } | |�j | | jd�} | j r�| �| �} | �| ||� | �| � | �| | j� | j | dd�S )Nr� r )�axes� concat_axisrG )�methodrP )rH �_indicator_pre_merger= r> �_get_join_info�_items_overlap_with_suffix� _info_axisrF r2 �_mgrre rG � _constructorZ __finalize__� _merge_type�_indicator_post_merge�_maybe_add_join_keys�_maybe_restore_index_levels�_maybe_drop_cross_columnr� ) r� � join_index� left_indexer� right_indexer�llabels�rlabels� lindexers� rindexers� result_data�typrm rN rN rO rL � s( z_MergeOperation.get_result�None)rm r� rJ c C s |d k r|j |dd� d S )NT)rQ �inplace)�drop)r� rm r� rN rN rO r� � s z(_MergeOperation._maybe_drop_cross_columnztuple[DataFrame, DataFrame])r= r> rJ c C s� |j �|j �}x"dD ]}||krtd|� ���qW | j|krDtd��|�� }|�� }d|d< |d �d�|d< d|d< |d �d�|d< ||fS ) N)�_left_indicator�_right_indicatorzECannot use `indicator=True` option when data contains a column named z:Cannot use name of an existing column for indicator columnr� r� Zint8r� r� )rQ �unionrw r� rG �astype)r� r= r> rQ �irN rN rO r� � s z$_MergeOperation._indicator_pre_merge)rm rJ c C s| |d � d�|d< |d � d�|d< t|d |d dddgd�|| j< || j j�dd d g�|| j< |jddgdd �}|S ) Nr� r r� r� r� r� )� categoriesZ left_onlyZ right_onlyZboth)�labelsr� )Zfillnar* r� �catZrename_categoriesr� )r� rm rN rN rO r� s z%_MergeOperation._indicator_post_mergec C sj g }xNt | j| j| j�D ]8\}}}| j�|�r| j�|�r||jjkr|� |� qW |rf|j |dd� dS )a Restore index levels specified as `on` parameters Here we check for cases where `self.left_on` and `self.right_on` pairs each reference an index level in their respective DataFrames. The joined columns corresponding to these pairs are then restored to the index of `result`. **Note:** This method has side effects. It modifies `result` in-place Parameters ---------- result: DataFrame merge result Returns ------- None T)r� N) �zipr� rA rB r� �_is_level_referencer� rd �namesre � set_index)r� rm Znames_to_restore�name�left_key� right_keyrN rN rO r� s z+_MergeOperation._maybe_restore_index_levelsznp.ndarray | None)rm r� r� rJ c s� d }d }t | j| j| j�}�x�t|�D �]�\}\�}} t|| �sBq&d\} } ��k�r|d k �r@|d k �r@�| jkr�|d kr�|dk�� }|r�| j| } t �� j | j� j �s�| j� j } nV�| j k�r@|d kr�|dk�� }|�r@| j | } t �� j | j � j ��s@| j � j } n.|d k �r@t| j | ��r@| j | } | j| } | d k �sR| d k r&| d k�rh�� j } n&t| dd�} t| j �} tj| || d�} | d k�r��� j }n&t| dd�} t| j �}tj| ||d�}|dk}|dk}|�� �r�t|�� |j }nF|d k �r|�� �rt| �� | j }n"t| ��| |�� t| j |j g�}�����r\t� |�jd���< q&�����r�t�jt ��r��� _ � ��fdd��jj D �}�j |dd � nt� �d ��_q&�� |��p�d |� �� � q&W d S ) N)NN���T)� extract_numpy)� fill_value)�dtyperd c s$ g | ] }|�kr �j �|�n� �qS rN )rd �get_level_values)rR � level_name)�key_colr� rm rN rO rX � s z8_MergeOperation._maybe_add_join_keys.<locals>.<listcomp>)r� )r� �key_)!r� r� rA rB � enumerate� _should_fillr= �anyr� r r� �_valuesr> r� r r0 r) �algos�take_ndr] r+ �wherer Z_is_label_referencer- rd r� rZ r, r� r� r� �insert)r� rm r� r� Zleft_has_missingZright_has_missing�keysr� �lname�rnameZ take_leftZ take_rightZlvalsZlfillZrvalsZrfillZ mask_leftZ mask_rightZ result_dtypeZidx_listrN )r� r� rm rO r� > sz z$_MergeOperation._maybe_add_join_keysz tuple[np.ndarray, np.ndarray]c C s t | j| j| j| jd�S )zreturn the join indexers)rE r? )�get_join_indexersr� r� rE r? )r� rN rN rO �_get_join_indexers� s z"_MergeOperation._get_join_indexersz2tuple[Index, np.ndarray | None, np.ndarray | None]c C s� | j j| j }| jj| j }| jrR| jrR| jdkrR|j|| jd| jd�\}}}�n\| jr�| jdkr�t ||| j | jd�\}}}�n.| jr�| jdkr�t ||| j | jd�\}}}�n | � � \}}| j�rt | j �dkr�| j| j j| jj|dd�}n(| jj�|�}tjd gt |� tjd �}n�| j�r�| jdk�rF| j| j j| jj|dd�}nTt | j�dk�rr| j| jj| j j|dd�}n(| j j�|�}tjd gt |� tjd �}ntt�t |���}t |�dk�r�|�t�}|||fS ) Nr� T)r? Zreturn_indexersrE r= )rE r> r )r? r� )r� )r= r� r� r> rC rD r? r rE �_left_join_on_indexr� r� r� rc �_create_join_indexrd r^ �np�array�intpr+ Zaranger� �object)r� �left_ax�right_axr� r� r� rN rN rO r� � sP z _MergeOperation._get_join_infor= r+ z np.ndarray)rd � other_index�indexerr? rJ c C sR | j |dfkrHt|t�sH|dk}t�|�rHt|jdd�}|�t|g��}|� |�S )a� Create a join index by rearranging one index to match another Parameters ---------- index : Index being rearranged other_index : Index used to supply values not found in index indexer : np.ndarray[np.intp] how to rearrange index how : str Replacement is only necessary if indexer based on other_index. Returns ------- Index ro r� F)�compat) r? rZ r, r� r� r) r� re r+ r^ )r� rd r� r� r? �maskr� rN rN rO r� � s z"_MergeOperation._create_join_indexc s� g }g }g }g }g }| j | j � �� fdd�}�fdd�}t| j��r�t| j��r��xtt| j| j�D �].\}} ||�r�|�|� || �r�|�| � |�d� n>| dk r�|���| �� |�| � n|��j� |��jj � qd|| ��sJ| dk �r|���| �� n |��j� |dk �rT|| k�rTt � �dk�r>|�| � n |�|� n |�| � |dk �rz|�� �|�� |�|� qd|�� j� |�� jj � qdW �n.t| j��r4xJ| jD ]@} || ��r�|�| � |�d� n|�� �| �� |�| � �q�W t | jjt ��r&dd� t| jjj | jjj�D �}n | jjjg}n�t| j��r�xJ| jD ]@} || ��rl|�| � |�d� n|���| �� |�| � �qHW t | j jt ��r�dd� t| j jj | j jj�D �}n | j jjg}|�r�| j �|�| _ |�r�| j�|�| _|||fS ) z� Note: has side effects (copy/delete key columns) Parameters ---------- left right on Returns ------- left_keys, right_keys c s t | �ot| �t� �kS )N)r rc )rr )r= rN rO ru ( rv z1_MergeOperation._get_merge_keys.<locals>.<lambda>c s t | �ot| �t� �kS )N)r rc )rr )r> rN rO ru ) rv Nr c S s g | ]\}}|j �|��qS rN )r� r^ )rR �lev� lev_codesrN rN rO rX e s z3_MergeOperation._get_merge_keys.<locals>.<listcomp>c S s g | ]\}}|j �|��qS rN )r� r^ )rR r� r� rN rN rO rX v s )r= r> �_anyrA rB r� re Z_get_label_or_level_valuesrd r� rc rZ r, �levels�codesr� Z_drop_labels_or_levels) r� � left_keys� right_keysr� Z right_dropZ left_dropZis_lkeyZis_rkey�lk�rk�krN )r= r> rO r� s~ z _MergeOperation._get_merge_keysc C sx �xpt | j| j| j�D �]X\}}}t|�r2t|�rt|�sDt|�rDqt|j�}t|j�}t|j�}t|j�}|r�|r�|�|�r�qn|s�|r�nt |j|j�r�qd|j� d|j� d�}t |j��r�t |j��r�|jj |jj kr�qn�t |j��r&t |j��r&||�|j�kt�|� �� st�dt� qnht |j��rnt |j��rn||�|j�kt�|� �� st�dt� qn tj|dd�tj|dd�k�r�q�n`|�r�t|j��s�t|j��r�|�r��n8|�r�t |j��s�t |j��r\|�r\tj|dd�} tj|dd�} ddd d g} d d d dd g} | | k�r*| | k�r*n0| | k�r>| | k�sR| | k�r�| | k�r�t|��n�t|j��r~t|j��s~t|��ntt|j��s�t|j��r�t|��nRt|j��r�t|j��s�t|��n0t|j��s�t|j��r�t|��n|�r�|�r�q|| jj k�r2|�r|j jnt } | jj f || j| �| �i�| _|| j j kr|�rL|j jnt } | j j f || j | �| �i�| _ qW d S )NzYou are trying to merge on z and z9 columns. If you wish to proceed you should use pd.concatziYou are merging on int and float columns where the float values are not equal to their int representationF)Zskipna�integerz mixed-integer�boolean�empty�string�unicode�mixed�bytes)!r� r� r� r� rc r r� r$ Z#_categories_match_up_to_permutationr r# �kindr r r� r� �isnanr] r� r� � UserWarningr Z infer_dtyper rw r% r r= rQ r� r� �assignr> )r� r� r� r� Z lk_is_catZ rk_is_catZ lk_is_objectZ rk_is_objectr� Z inferred_leftZinferred_rightZ bool_types� string_typesr� rN rN rO r� � s� z(_MergeOperation._maybe_coerce_merge_keysz%tuple[DataFrame, DataFrame, str, str]c C s: dt �� �� � �}d}|jf |di�|jf |di�||fS )a Creates the configuration to dispatch the cross operation to inner join, e.g. adding a join column and resetting parameters. Join column is added to a new object, no inplace modification Parameters ---------- left : DataFrame right : DataFrame Returns ------- a tuple (left, right, how, cross_col) representing the adjusted DataFrames with cross_col, the merge operation set to inner and the column to join over. Z_cross_r7 r� )�hashlib�md5� hexdigestr )r� r= r> r� r? rN rN rO r� s z+_MergeOperation._create_cross_configurationc C s� | j dkr@| js4| js4| jd k s4| jd k s4| jd k r<td��d S | jd k�r2| jd k�r2| jd k�r2| jr~| jr~d\| _| _n�| jr�td��n�| jr�td��n�| jj}| j j}|� |�}t |�dkr�td| j� d| j� d | j� d | j� ���|j |d d �j �r|j |d d �j �s"td t|�� ���| | _| _�n>| jd k �r�| jd k �sV| jd k �r^td��| j�sn| j�rvtd��| j | _| _n�| jd k �r�| j�r�td��| j�s�| jd k�r�td��t | j�}| j�rpt | j�| j jjk�r�td��d g| | _nt| jd k �rp| j�rtd��| j�s4| jd k�r4td��t | j�}| j�rpt | j�| jjjk�rdtd��d g| | _| j dk�r�t | j�t | j�k�r�td��d S )Nr� zMCan not pass on, right_on, left_on or set right_index=True or left_index=True)rN rN z&Must pass right_on or right_index=Truez$Must pass left_on or left_index=Truer z>No common columns to perform merge on. Merge options: left_on=z , right_on=z , left_index=z, right_index=r7 )r? zData columns not unique: zSCan only pass argument "on" OR "left_on" and "right_on", not a combination of both.zYCan only pass argument "on" OR "left_index" and "right_index", not a combination of both.z:Can only pass argument "left_on" OR "left_index" not both.z&Must pass "right_on" OR "right_index".zDlen(left_on) must equal the number of levels in the index of "right"z<Can only pass argument "right_on" OR "right_index" not both.z$Must pass "left_on" OR "left_index".zDlen(right_on) must equal the number of levels in the index of "left"z%len(right_on) must equal len(left_on))r? rC rD rB rA r@ r r= rQ r> � intersectionrc r � is_unique�reprrd r� rw )r� Z left_colsZ right_colsZ common_cols�nrN rN rO r� % sx $ & "z'_MergeOperation._validate_specification)rI rJ c C s� | j r| jjj}nt�| j�j}| jr2| jjj}nt�| j �j}|dkrv|sZ|sZt d��q�|sht d��q�|s�t d��n>|dkr�|s�t d��n(|dkr�|s�t d��n|d kr�nt d ��d S ) N)Z one_to_onez1:1zQMerge keys are not unique in either left or right dataset; not a one-to-one mergezAMerge keys are not unique in left dataset; not a one-to-one mergezBMerge keys are not unique in right dataset; not a one-to-one merge)Z one_to_manyz1:mzBMerge keys are not unique in left dataset; not a one-to-many merge)Z many_to_onezm:1zCMerge keys are not unique in right dataset; not a many-to-one merge)Z many_to_manyzm:mz!Not a valid argument for validate) rC r� rd r r, � from_arraysr� rD r� r� r rw )r� rI Z left_uniqueZ right_uniquerN rN rO r� | s6 z_MergeOperation._validate) r7 NNNr� FFTr� TFN)r= )�__name__� __module__� __qualname__�__doc__r� r� rL r� r� r� r� r� r� r� r� r� r� r� r� r� rN rN rN rO rK Z s: *V" #kC v WrK z tuple[np.ndarray, np.ndarray])rE r? rJ c s� t ��t ��kstd��� ���fdd�tt ���D �}t|� }dd� |D �\}}} t||| ��\} } t| | �� d�\} } } t�|�}� dkr��|d< tjtj dd � tj d �� } | | | | f|�S ) ac Parameters ---------- left_keys : ndarray, Index, Series right_keys : ndarray, Index, Series sort : bool, default False how : {'inner', 'outer', 'left', 'right'}, default 'inner' Returns ------- np.ndarray[np.intp] Indexer into the left_keys. np.ndarray[np.intp] Indexer into the right_keys. z/left_key and right_keys must be the same lengthc 3 s& | ] }t �| �| �� d �V qdS ))rE r? N)�_factorize_keys)rR r )r? r� r� rE rN rO rT � s z$get_join_indexers.<locals>.<genexpr>c s s | ]}t |�V qd S )N)r[ )rR rr rN rN rO rT � s )rE r? )r= r> rE c [ s t j|| |f|�d d d� S )Nr� )�libjoin�left_outer_join)rr rs �count�kwargsrN rN rO ru � s z#get_join_indexers.<locals>.<lambda>)r7 r= r> ro ) rc �AssertionErrorrb r� �_get_join_keysr rG r Z inner_joinr Zfull_outer_join)r� r� rE r? r �mapped�zipped�llab�rlab�shape�lkey�rkeyr Z join_funcrN )r? r� r� rE rO r� � s$ r� r, r+ z np.ndarrayz.tuple[list[Index], np.ndarray, list[Hashable]])r= r> r� �lindexer�rindexerrJ c C s� ddd�dd�}||�}|j }|j}|j} |dkr<t| j�}|dkrNt|j�}xv|D ]n} | | jkrl| } |} n|} |} | j�| �} | j | }| j| }tj|| dd�}||g }||g }| | g } qTW ||| fS ) a� *this is an internal non-public method* Returns the levels, labels and names of a multi-index to multi-index join. Depending on the type of join, this method restores the appropriate dropped levels of the joined multi-index. The method relies on lidx, rindexer which hold the index positions of left and right, where a join was feasible Parameters ---------- left : MultiIndex left index right : MultiIndex right index dropped_level_names : str array list of non-common level names join_index : Index the index of the join between the common levels of left and right lindexer : np.ndarray[np.intp] left indexer rindexer : np.ndarray[np.intp] right indexer Returns ------- levels : list of Index levels of combined multiindexes labels : intp array labels of combined multiindexes names : List[Hashable] names of combined multiindex levels r+ r, )rd rJ c S s( t | t�r| S tj| jg| jgd�S d S )N)r� )rZ r, r r� r� )rd rN rN rO �_convert_to_multiindex s z@restore_dropped_levels_multijoin.<locals>._convert_to_multiindexNr� )r� )r� r� r� rb �sizerd r� r� )r= r> Zdropped_level_namesr� r" r# r$ Z join_levelsZ join_codesr� Zdropped_level_name�idxr� Zname_idxZrestore_levelsr� Z restore_codesrN rN rO � restore_dropped_levels_multijoin� s. , r' c @ sD e Zd ZdZdddd d d d d d d d d dd� dd�Zdd�dd�ZdS )rq Z ordered_mergeNFr� �r9 r: Tro zDataFrame | SerieszIndexLabel | Noner< r� r z str | Noner; ) r= r> r@ rA rB rC rD r� rF rG rp r? c C s, | | _ tj| ||||||||| | dd� d S )NT) r@ rA rC rD rB r� r? rF rE )rp rK r� ) r� r= r> r@ rA rB rC rD r� rF rG rp r? rN rN rO r� F s z_OrderedMerge.__init__r4 )rJ c C s� | � � \}}}t| jj| jj| j�\}}| jdkrHt�|�}t�|�}n|}|}|d k r`d|ini }|d k rtd|ini } t | jj |f| jj | fg|� |�|gd| j d�} | jj } | | �} | �| ||� | S )NZffillr� r )r� r� rG )r� r� r= r� r> rF rp r Z ffill_indexerr2 r� re rG r� r� ) r� r� r� r� r� r� Zleft_join_indexerZright_join_indexerr� r� r� r� rm rN rN rO rL f s* z_OrderedMerge.get_result) NNNFFr� r( TNro )r r r r� r� rL rN rN rN rO rq C s &rq )r� c C s d| � �}t t|d �S )N� asof_join_)�getattrr )r� r� rN rN rO �_asof_function� s r+ c C s d| � d�}t t|d �S )Nr) Z _on_X_by_Y)r* r )r� r� rN rN rO �_asof_by_function� s r, )�int64_t�doubler� r )r� rJ c C s t | �r dS t| �rdS dS dS )z2Upcast a dtype to 'int64_t', 'double', or 'object'r- r. r� N)r r )r� rN rN rO �_get_cython_type_upcast� s r/ c sj e Zd ZdZd d d d d d d d d d d ddd dd�dd�Zdd�� fdd� Z� fdd�Zdd�dd�Z� ZS ) r� Z asof_mergeNFr� �r9 r: Tr� r~ zDataFrame | SerieszIndexLabel | Noner< r� r z str | Noner; )r= r> r@ rA rB rC rD r� rF rG rp r? r r� c C sJ || _ | | _| | _|| _|| _|| _tj| |||||||| || |d� d S )N) r@ rA rB rC rD r� r? rF rp )rf ry rz r� r r� rq r� )r� r= r> r@ rA rB rC rD rf ry rz r� rF rG rp r? r� r r� rN rN rO r� � s& z_AsOfMerge.__init__r� )rJ c sb t � �� t| j�dkr&| js&td��t| j�dkrB| jsBtd��| jr^t| j j t �r^td��| jrzt| j j t �rztd��| j d k r�| jd k s�| jd k r�td��| j | _| _| jd kr�| jd k r�td��| jd k r�| jd kr�td��| j�s4| jd }t|��r |j}n(|| j jk�r$| j | jn | j j �|�}n | j j j}| j�s�| jd }t|��rb|j}n(|| j jk�r|| j | jn | j j �|�}n | j j j}t|��s�t|��r�td t|�� d t|�� d ���| jd k �rBt| j��s�| jg| _t| j��s | jg| _t| j�t| j�k�r td ��| jt| j� | _| jt| j� | _| jdk�r^td| j� ���d S )Nr� z can only asof on a key for leftz can only asof on a key for rightz left can only have one indexz right can only have one indexz(Can only pass by OR left_by and right_byzmissing left_byzmissing right_byr zIncompatible merge dtype, z and z$, both sides must have numeric dtypez(left_by and right_by must be same length)r~ �forwardZnearestzdirection invalid: )�superr� rc rA rC r rB rD rZ r= rd r, r> rf ry rz r r� rQ r� r$ r r! r[ r� )r� rA Zlo_dtyperB Zro_dtype)� __class__rN rO r� � sZ z"_AsOfMerge._validate_specificationc s� t � �� \}}}x�tt||��D ]�\}\}}t|j|j�s t|j�rtt|j�rtd|� dt|j�� dt|j�� d�}n$d|� dt|j�� dt|j�� d�}t|��q W | j d k �r�| j r�| j j }n|d }d| j � dt|j�� �}t |��r t| j tj��st|��| j td �k �r�td ��npt|��rPt| j ��s:t|��| j d k �r�td ��n<t|��r�t| j ��snt|��| j d k �r�td ��ntd ��t| j��s�d | j� �}t|��|||fS ) Nzincompatible merge keys [z] z and z), both sides category, but not equal onesz, must be the same typer� zincompatible tolerance z, must be compat with type r ztolerance must be positivez'key must be integer, timestamp or floatz,allow_exact_matches must be boolean, passed )r2 r� r� r� r r� r r r r� rC r= rd r% rZ �datetime� timedeltar r r r r" r r ) r� r� r� r� r� r� r� r� �lt)r3 rN rO r� ( s@ &$ z_AsOfMerge._get_merge_keysz tuple[np.ndarray, np.ndarray]c C s� dd�dd�}| j r | jjjn| jd }| jr8| jjjn| jd }| j}t |�j s�d}t |�� � rtt d|� d���nt |� d ���t |�j s�d }t |�� � r�t d|� d���nt |� d ���t|�r�|�d �}|�d �}|d k r�t|�}|j}| jd k �r�| j �r| j�r| j}| j}n | jd d� }| jd d� }t|�dk�rT|d }|d }n||�}||�}t|j�}t| } | |�}| |�}t| j�} | ||||| j|�S t| j�} | ||| j|�S d S )zreturn the join indexersz np.ndarray)rJ c S sT dd� | D �} t tjdt| �� �}dd� | D �}t t||��}t�t t| � �|�S )z4unlike np.transpose, this returns an array of tuplesc S s$ g | ] }t |�s|n t|��� �qS rN )r r0 Z_values_for_argsort)rR rr rN rN rO rX y s z?_AsOfMerge._get_join_indexers.<locals>.flip.<locals>.<listcomp>Nc S s g | ] }|j �qS rN )r� )rR rr rN rN rO rX s )r[ r� �ascii_lowercaserc r� r� r� )Zxsr� ZdtypesZlabeled_dtypesrN rN rO �flipt s z+_AsOfMerge._get_join_indexers.<locals>.flipr� r= z"Merge keys contain null values on z sidez keys must be sortedr> �i8Nr r� )rC r= rd r� r� rD r> r� r� r+ Z is_monotonicr( r� rw r% �viewr �valuery rc r/ r� � _type_castersr, r� r r+ ) r� r8 Z left_valuesZ right_valuesr� ZsideZleft_by_valuesZright_by_valuesZby_typeZby_type_caster�funcrN rN rO r� p sZ z _AsOfMerge._get_join_indexers)NNNFFNNNr� r0 TNr� NTr~ ) r r r r� r� r� r� r� � __classcell__rN rN )r3 rO r� � s( *M Hr� )rd rE rJ c s( � ��fdd�t � j�D �}t|� }dd� |D �\}}}�rRtttj|� j��}ndd� }tt|� j��}x�t t���D ]t} � j| dk} | � � rx�| || || d k } | j dks�| d | d ks�|| d7 < || d || | < qxW t |||��\} } t | | �d �\} } }t j| | |�d �S ) Nc 3 s& | ] }t � j| �| �d �V qdS ))rE N)r r� )rR r )rd � join_keysrE rN rO rT � s z*_get_multiindex_indexer.<locals>.<genexpr>c s s | ]}t |�V qd S )N)r[ )rR rr rN rN rO rT � s c S s | j dddd�S )Nr9 FT)�subokrG )r� )�arN rN rO ru � rv z)_get_multiindex_indexer.<locals>.<lambda>r� r� r )rE )rb r� r� r[ �mapr� r^ r� rc r� r% r r r r )r? rd rE r r ZrcodesZlcodesr Zi8copyr� r� rA r r! r rN )rd r? rE rO �_get_multiindex_indexer� s$ rC c C s( t | |j|d�\}}}tj||||d�S )N)rE )r r� r r )Zjoin_keyrd rE r� r� r rN rN rO �_get_single_indexer� s rD z+tuple[Index, np.ndarray | None, np.ndarray])r� r� rE rJ c C s� t |�dkr@t|t�r$t |�|jks,td��t|||d�\}}n|d }t|||d�\}}|snt | �t |�kr�| �|�}|||fS | d |fS )Nr� z�If more than one join key is given then 'right_ax' must be a MultiIndex and the number of join keys must be the number of levels in right_ax)rE r )rc rZ r, r� r rC rD r^ )r� r� r? rE r� r� Zjkeyr� rN rN rO r� � s r� r z"tuple[np.ndarray, np.ndarray, int])r� r� rE r? rJ c C sr t | ddd�} t |ddd�}t| j�rJt|j�rJtd| �j} td|�j}n�t| j�r�t|j�r�t| j|j�r�t| t�szt �t|t�s�t �| � |�}t | j �} t |j �}n0t| t �r�t| j|j�r�| �� \} }|�� \}}t| j��rt|j��rtj}t t�| ��} t t�|��}nbt| j��r`t| j|j��r`tj}t tj| tjd��} t tj|tjd��}ntj}t| �} t|�}|tt| �t|���}|�| �}|�|�}|jtjk�s�t |j��|jtjk�s�t |j��|� � } |�r�|j � � } t | ||�\}}|dk} | � � } |dk} | � � }| �s$|�rT| �r8t�!|| | � |�rLt�!|| | � | d7 } |dk�rh||| fS ||| fS )u? Encode left and right keys as enumerated types. This is used to get the join indexers to be used when merging DataFrames. Parameters ---------- lk : array-like Left key. rk : array-like Right key. sort : bool, defaults to True If True, the encoding is done such that the unique elements in the keys are sorted. how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’ Type of merge. Returns ------- np.ndarray[np.intp] Left (resp. right if called with `key='right'`) labels, as enumerated type. np.ndarray[np.intp] Right (resp. left if called with `key='right'`) labels, as enumerated type. int Number of unique elements in union of left and right labels. See Also -------- merge : Merge DataFrame or named Series objects with a database-style join. algorithms.factorize : Encode the object as an enumerated type or categorical variable. Examples -------- >>> lk = np.array(["a", "c", "b"]) >>> rk = np.array(["a", "c"]) Here, the unique values are `'a', 'b', 'c'`. With the default `sort=True`, the encoding will be `{0: 'a', 1: 'b', 2: 'c'}`: >>> pd.core.reshape.merge._factorize_keys(lk, rk) (array([0, 2, 1]), array([0, 2]), 3) With the `sort=False`, the encoding will correspond to the order in which the unique elements first appear: `{0: 'a', 1: 'c', 2: 'b'}`: >>> pd.core.reshape.merge._factorize_keys(lk, rk, sort=False) (array([0, 1, 2]), array([0, 1]), 3) T)r� Z extract_ranger5 )r� r� r� r> )"r0 r r� r Z_ndarrayr r rZ r* r Z_encode_with_my_categoriesr r� r/ Z_values_for_factorizer � libhashtableZInt64Factorizerr� Zasarrayr% Zint64ZObjectFactorizerr �maxrc Z factorizer� Z get_count�uniquesZto_array� _sort_labelsr� Zputmask)r� r� rE r? r| �klassZrizerr r r rG ZlmaskZlanyZrmaskZranyrN rN rO r sb 6 r )rG r= r> rJ c C s\ t |�}t�||g�}tj| |dd�\}}|jtjks:t�|d |� ||d � }}||fS )Nr� )Z na_sentinel)rc r� Z concatenater� Z safe_sortr� r� r ) rG r= r> Zllengthr� r| Z new_labelsZnew_leftZ new_rightrN rN rO rH � s rH )rE c s, t � fdd�tt� �dd�D ��}tj� d|� dd�}|| d jdddd � }||d jdddd � }xVtd|�D ]H}tjd d �� |� | }W d Q R X || | | 7 }||| | 7 }qtW |t� �kr�||fS t|||d �\}}} |g| |d � } |g||d � }| g� |d � � t| |� |�S ) Nc 3 s" | ]}t � d |� �s|V qd S )N)r3 )rR r� )r rN rO rT � s z!_get_join_keys.<locals>.<genexpr>r r� r� r9 )r� F)r@ rG �ignore)�divide)rE ) �nextrb rc r� �prodr� Zerrstater r ) r r r rE ZnlevZstrider r! r� r rN )r rO r � s$ r )rJ c C s t | t�rt |t�sdS | |kS )NT)rZ r; )r� r� rN rN rO r� � s r� c C s | d k ot j| � S )N)r� Z any_not_none)rr rN rN rO r� � s r� r )�objrJ c C sN t | t�r| S t | t�r6| jd kr,td��qJ| �� S ntdt| �� d���d S )Nz$Cannot merge a Series without a namez.Can only merge Series or DataFrame objects, a z was passed)rZ r& r' r� rw Zto_frame� TypeErrorr� )rN rN rN rO r� � s r� ztuple[Index, Index])r= r> rF rJ c s t |dd�s(tjdt|�� d�tdd� | �|�� t� �dkrF| |fS |\}}|sd|sdtd� � ���� fd d �}t||d �}t||d �}| � |�}|� |�} g } |j s�||� � | � � @ � � } | j s�| � | | � � |� � @ � � � | �rtjd t| �� d �tdd� || fS )z� Suffixes type validation. If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, the entry is simply converted to string. F)Z allow_setszPassing 'suffixes' as a z�, is not supported and may give unexpected results. Provide 'suffixes' as a tuple instead. In the future a 'TypeError' will be raised.� )r� r z)columns overlap but no suffix specified: c s | � kr |dk r | � |� �S | S )a? Rename the left and right indices. If there is overlap, and suffix is not None, add suffix, otherwise, leave it as-is. Parameters ---------- x : original column name suffix : str or None Returns ------- x : renamed column name NrN )rr �suffix)� to_renamerN rO �renamer s z+_items_overlap_with_suffix.<locals>.renamer)rQ z1Passing 'suffixes' which cause duplicate columns zM in the result is deprecated and will raise a MergeError in a future version.)r! r� r� r� r� r rc rw r Z_transform_indexr Z duplicatedra �extendrU ) r= r> rF ZlsuffixZrsuffixrS ZlrenamerZrrenamerr� r� ZdupsrN )rR rO r� � s6 r� ) r7 NNNFFFr8 TFN)NNNNNNr8 ro ) NNNFFNNNr8 NTr~ )Fr7 )F)F)Tr7 )nr � __future__r rG r4 � functoolsr r r� �typingr r r r� �numpyr� Z pandas._libsr r rE r r r Zpandas._typingr r r r r Z pandas.errorsr Zpandas.util._decoratorsr r Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r r r r r r r r r r r r! r" r# r$ r% Zpandas.core.dtypes.genericr& r' Zpandas.core.dtypes.missingr( r) Zpandasr* r+ r, r- Z pandas.corer. Zpandas.core.algorithms�coreZ algorithmsr� Zpandas.core.arraysr/ Zpandas.core.common�commonr� Zpandas.core.constructionr0 Zpandas.core.framer1 Zpandas.core.internalsr2 Zpandas.core.sortingr3 r4 r5 rP rn r} r� rK r� r' rq r+ r, r< r/ r� rC rD r� r rH r r� r� r� r� rN rN rN rO �<module> s� P . ; $ Z7^N #( !
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